The Experts below are selected from a list of 40947 Experts worldwide ranked by ideXlab platform
Gijs Dubbelman - One of the best experts on this subject based on the ideXlab platform.
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monocular semantic occupancy grid Mapping with convolutional variational encoder decoder networks
International Conference on Robotics and Automation, 2019Co-Authors: Marinus J G Van De Molengraft, Gijs DubbelmanAbstract:In this letter, we research and evaluate end-to-end learning of monocular semantic-metric occupancy grid Mapping from weak binocular ground truth. The network learns to predict four classes, as well as a camera to bird's eye view Mapping. At the core, it utilizes a variational encoder–decoder network that encodes the front-view visual information of the driving scene and subsequently decodes it into a two-dimensional top-view Cartesian coordinate system. The evaluations on Cityscapes show that the end-to-end learning of semantic-metric occupancy grids outperforms the deterministic Mapping approach with flat-plane assumption by more than 12% mean intersection-over-union. Furthermore, we show that the variational sampling with a relatively small embedding vector brings robustness against vehicle dynamic perturbations, and generalizability for unseen KITTI data. Our network achieves real-time inference rates of approx. 35 Hz for an input image with a resolution of 256 × 512 pixels and an Output Map with 64 × 64 occupancy grid cells using a Titan V GPU.
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monocular semantic occupancy grid Mapping with convolutional variational encoder decoder networks
arXiv: Robotics, 2018Co-Authors: Marinus J G Van De Molengraft, Gijs DubbelmanAbstract:In this work, we research and evaluate end-to-end learning of monocular semantic-metric occupancy grid Mapping from weak binocular ground truth. The network learns to predict four classes, as well as a camera to bird's eye view Mapping. At the core, it utilizes a variational encoder-decoder network that encodes the front-view visual information of the driving scene and subsequently decodes it into a 2-D top-view Cartesian coordinate system. The evaluations on Cityscapes show that the end-to-end learning of semantic-metric occupancy grids outperforms the deterministic Mapping approach with flat-plane assumption by more than 12% mean IoU. Furthermore, we show that the variational sampling with a relatively small embedding vector brings robustness against vehicle dynamic perturbations, and generalizability for unseen KITTI data. Our network achieves real-time inference rates of approx. 35 Hz for an input image with a resolution of 256x512 pixels and an Output Map with 64x64 occupancy grid cells using a Titan V GPU.
Lalo Magni - One of the best experts on this subject based on the ideXlab platform.
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a receding horizon approach to the nonlinear h control problem
Automatica, 2001Co-Authors: Lalo Magni, H Nijmeijer, Van Der Arjan SchaftAbstract:The receding–horizon (RH) methodology is extended to the design of a robust controller of H∞ type for nonlinear systems. Using the nonlinear analogue of the Fake H∞ algebraic Riccati equation, we derive an inverse optimality result for the RH schemes for which increasing the horizon causes a decrease of the optimal cost function. This inverse optimality result shows that the input–Output Map of the closed-loop system obtained with the RH control law has a bounded L2-gain. Robustness properties of the nonlinear H∞ control law in face of dynamic input uncertainty are considered.
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brief a receding horizon approach to the nonlinear h control problem
Automatica, 2001Co-Authors: Lalo Magni, H Nijmeijer, A J Van Der SchaftAbstract:The receding-horizon (RH) methodology is extended to the design of a robust controller of H"~ type for nonlinear systems. Using the nonlinear analogue of the Fake H"~ algebraic Riccati equation, we derive an inverse optimality result for the RH schemes for which increasing the horizon causes a decrease of the optimal cost function. This inverse optimality result shows that the input-Output Map of the closed-loop system obtained with the RH control law has a bounded L"2-gain. Robustness properties of the nonlinear H"~ control law in face of dynamic input uncertainty are considered.
Marinus J G Van De Molengraft - One of the best experts on this subject based on the ideXlab platform.
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monocular semantic occupancy grid Mapping with convolutional variational encoder decoder networks
International Conference on Robotics and Automation, 2019Co-Authors: Marinus J G Van De Molengraft, Gijs DubbelmanAbstract:In this letter, we research and evaluate end-to-end learning of monocular semantic-metric occupancy grid Mapping from weak binocular ground truth. The network learns to predict four classes, as well as a camera to bird's eye view Mapping. At the core, it utilizes a variational encoder–decoder network that encodes the front-view visual information of the driving scene and subsequently decodes it into a two-dimensional top-view Cartesian coordinate system. The evaluations on Cityscapes show that the end-to-end learning of semantic-metric occupancy grids outperforms the deterministic Mapping approach with flat-plane assumption by more than 12% mean intersection-over-union. Furthermore, we show that the variational sampling with a relatively small embedding vector brings robustness against vehicle dynamic perturbations, and generalizability for unseen KITTI data. Our network achieves real-time inference rates of approx. 35 Hz for an input image with a resolution of 256 × 512 pixels and an Output Map with 64 × 64 occupancy grid cells using a Titan V GPU.
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monocular semantic occupancy grid Mapping with convolutional variational encoder decoder networks
arXiv: Robotics, 2018Co-Authors: Marinus J G Van De Molengraft, Gijs DubbelmanAbstract:In this work, we research and evaluate end-to-end learning of monocular semantic-metric occupancy grid Mapping from weak binocular ground truth. The network learns to predict four classes, as well as a camera to bird's eye view Mapping. At the core, it utilizes a variational encoder-decoder network that encodes the front-view visual information of the driving scene and subsequently decodes it into a 2-D top-view Cartesian coordinate system. The evaluations on Cityscapes show that the end-to-end learning of semantic-metric occupancy grids outperforms the deterministic Mapping approach with flat-plane assumption by more than 12% mean IoU. Furthermore, we show that the variational sampling with a relatively small embedding vector brings robustness against vehicle dynamic perturbations, and generalizability for unseen KITTI data. Our network achieves real-time inference rates of approx. 35 Hz for an input image with a resolution of 256x512 pixels and an Output Map with 64x64 occupancy grid cells using a Titan V GPU.
A J Van Der Schaft - One of the best experts on this subject based on the ideXlab platform.
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brief a receding horizon approach to the nonlinear h control problem
Automatica, 2001Co-Authors: Lalo Magni, H Nijmeijer, A J Van Der SchaftAbstract:The receding-horizon (RH) methodology is extended to the design of a robust controller of H"~ type for nonlinear systems. Using the nonlinear analogue of the Fake H"~ algebraic Riccati equation, we derive an inverse optimality result for the RH schemes for which increasing the horizon causes a decrease of the optimal cost function. This inverse optimality result shows that the input-Output Map of the closed-loop system obtained with the RH control law has a bounded L"2-gain. Robustness properties of the nonlinear H"~ control law in face of dynamic input uncertainty are considered.
Van Der Arjan Schaft - One of the best experts on this subject based on the ideXlab platform.
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a receding horizon approach to the nonlinear h control problem
Automatica, 2001Co-Authors: Lalo Magni, H Nijmeijer, Van Der Arjan SchaftAbstract:The receding–horizon (RH) methodology is extended to the design of a robust controller of H∞ type for nonlinear systems. Using the nonlinear analogue of the Fake H∞ algebraic Riccati equation, we derive an inverse optimality result for the RH schemes for which increasing the horizon causes a decrease of the optimal cost function. This inverse optimality result shows that the input–Output Map of the closed-loop system obtained with the RH control law has a bounded L2-gain. Robustness properties of the nonlinear H∞ control law in face of dynamic input uncertainty are considered.
